# WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation

arXiv: https://arxiv.org/abs/2011.09162 · HTML: https://mlsanity.com/paper/2011.09162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition](https://mlsanity.com/paper/2307.02909/) (2023)
- [A Time-domain Real-valued Generalized Wiener Filter for Multi-channel Neural Separation Systems](https://mlsanity.com/paper/2112.03533/) (2021)

## Abstract (arXiv)

This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recognition (ASR) backend. While the recently proposed Weighted Power minimization Distortionless response (WPD) beamformer can perform separation and dereverberation simultaneously, the noise cancellation component still has the potential to progress. We propose an improved neural WPD beamformer called "WPD++" by an enhanced beamforming module in the conventional WPD and a multi-objective loss function for the joint training. The beamforming module is improved by utilizing the spatio-temporal correlation. A multi-objective loss, including the complex spectra domain scale-invariant signal-to-noise ratio (C-Si-SNR) and the magnitude domain mean square error (Mag-MSE), is properly designed to make multiple constraints on the enhanced speech and the desired power of the dry clean signal. Joint training is conducted to optimize the complex-valued mask estimator and the WPD++ beamformer in an end-to-end way. The results show that the proposed WPD++ outperforms several state-of-the-art beamformers on the enhanced speech quality and word error rate (WER) of ASR.